AI
AI: A New Era for Deathcare Ethics
What place does AI have within the deathcare and funeral industry, what are the benefits and how can we avoid the potential pitfalls?
Can AI Help Us to Make Better Decisions?
Find out how AI-powered tools can assist deathcare providers in making data-driven decisions and optimizing operations.
Graeme Arthur
Jul 15, 2024
| Read time: 4 mins
AI is revolutionizing our ways of working.
Whether you see this as an existential threat, or the greatest thing since the invention of the microchip (themselves with a role to play in the advancement of AI [1]), AI is here to stay, and organizations are only beginning to realize its full potential.
That of course, doesn’t come without due consideration of issues surrounding bias, privacy, ethics, security and so on, (some of which we discuss here) - these big, bold zeitgeist-challenging topics with much broader societal implications.
However, generative AI in particular - the strand using data-trained deep learning models to generate content [2] - is already influencing the deathcare sphere; from AI-assisted obituary writers that enhance customer service, to the myriad of new ways of connecting families to loved ones, such as the creation digital legacies, to so-called ‘grief bots’, as well as the boundary-pushing concepts of fully digital avatars.
But, another application of AI is in its ability to process large amounts of data. And that has real implications for the here and now.
Predicting the future
Without the aid of a DeLorean and an eccentric scientist, it can be hard for most of us to see into the future.
For that, we need data. And lots of it.
Something of a buzzword in recent times, ‘Big data’ refers to large, complex and ever-growing sets of data.
Some examples of ‘big data’ include customer survey data, social media feeds and web page content; information that is all around us, and getting bigger every day. That data can be used in a number of ways, including in predictive analytics, user behavior analysis and customer experience optimization.[3]
Aside from requiring these large data sets to be trained, AI [with the aid of data structures to store, manipulate and manage them [4]] is very good at analyzing these large volumes of data.
That allows us to observe patterns and extract insights to help predict emerging trends or customer behaviors [5] and support decision-making that can both help to sustain longevity and long-term growth, as well as maintaining a competitive advantage.
Big data analysis isn’t without its challenges - there are issues of privacy and regulatory control, and of course, inaccurate data may call into question the veracity of the results produced by the predictive models and machine learning algorithms they’re trained on.
But the point here is that decisions that are based on data insights are arguably better ones. Why?
Data-driven decision making
While decisions based on intuition aren’t necessarily inherently bad, insights gained from analyzing available data can help to remove guesswork and make them more objective.
In practice, that data needs to be collected, organized, verified and analyzed - again, something that AI is very good at.
This ability to identify trends or patterns over time also allows us to not only gain a deeper understanding of any current issues we may be facing, but to put plans in place to overcome them.
Ultimately, however, it allows us to become more strategic in our approach, so we might more easily determine where our focus needs to be.
Read more about data-driven decision-making for deathcare providers.
AI-driven decision making
There is perhaps a distinction to be made here between what we might think of as ‘data-driven’ decision making and ‘AI-driven’ decision making.
As the Harvard Business review suggests [6], incorporating AI may serve as an evolutionary next step beyond data-driven workflows for routine decisions based on structured data, and which, as they describe are, “less prone to human’s cognitive bias.”
Here, they argue that AI is better at making decisions than what we as humans can do alone in objective settings, creating what they call a, “step change improvement in efficiency.”; this however, while recognizing that there are many decisions that depend on more than just structured data - market dynamics for example, which through a convergence of the two, may allow us to make better decisions than by using one or the other.
AI and deathcare
So how might we employ AI in terms of better decision making within the deathcare sphere?
We’re already seeing AI employed within PlotBox’s solutions to provide a greater level of inventory intelligence - utilizing AI technologies including smart data verification and audit reports, that help to integrate data in a way that can help to identify risk and uncover hidden inventory.
We are also beginning to see how the utilization of AI-powered Optical Character Recognition using Natural language Processing (NLP) - [combining computational linguistics with learning models] can analyze and extract text, not only enhancing process automation, but potentially delivering insights and uncovering hidden histories.
Data-driven insights
More broadly, AI may also help look for trends and patterns using historical data that can help to manage resources more effectively, or forecast demand for services - enabling more proactive planning; in terms of personalization, it also has the potential to give insights into customer preferences in order to offer more tailored services.
In all, then, we begin to see potential for saving both time and money, optimizing operations, improving customer service and helping us to make more informed decisions, more easily.
As AI evolves, so too does its potential to help us to make better choices based on the information provided - however, not for nothing, many of those choices still require the nuance that only we can bring.